RAG over Thinking Traces Can Improve Reasoning Tasks

The paper introduces a novel approach to enhance reasoning tasks in AI by utilizing retrieval-augmented generation (RAG) with thinking traces—intermediate thinking trajectories from problem-solving attempts—rather than traditional document retrieval. The proposed T3 method converts these traces into structured representations, leading to significant performance improvements on benchmarks like AIME 2025-2026, with relative gains of +56.3% for Gemini-2.5-Flash and notable improvements for other models as well. This research indicates that leveraging thinking traces as a retrieval corpus can substantially enhance reasoning capabilities in AI systems, making it a valuable strategy for practitioners working with LLMs.